
import numpy as np                                       ##for Mathematical and logical operations
import pandas as pd                                      ##to load,organize,manipulate,model and analyze the data
import matplotlib.pyplot as plt                          ##for plotting graphs 
import seaborn as sns                                    ##to visualize random distributions
import warnings
warnings.filterwarnings('ignore')


## Getting the dataset
data = pd.read_csv('crimes_against_women_2001-2014.csv')
data.head()


## Getting first 5 rows
data.head(5)


## Getting last 5 rows
data.tail(5)


## Check the rows and columns in the dataset
data.shape

(10677, 11)


## To check the total size of the data(rows*column = size)
data.size

117447


## To get all the columns present in the dataset

data.columns.to_list()

['Unnamed: 0',
 'STATE/UT',
 'DISTRICT',
 'Year',
 'Rape',
 'Kidnapping and Abduction',
 'Dowry Deaths',
 'Assault on women with intent to outrage her modesty',
 'Insult to modesty of Women',
 'Cruelty by Husband or his Relatives',
 'Importation of Girls']


## TO get the summary on the dataframe including dtype,shape and memory storage
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10677 entries, 0 to 10676
Data columns (total 11 columns):
 #   Column                                               Non-Null Count  Dtype 
---  ------                                               --------------  ----- 
 0   Unnamed: 0                                           10677 non-null  int64 
 1   STATE/UT                                             10677 non-null  object
 2   DISTRICT                                             10677 non-null  object
 3   Year                                                 10677 non-null  int64 
 4   Rape                                                 10677 non-null  int64 
 5   Kidnapping and Abduction                             10677 non-null  int64 
 6   Dowry Deaths                                         10677 non-null  int64 
 7   Assault on women with intent to outrage her modesty  10677 non-null  int64 
 8   Insult to modesty of Women                           10677 non-null  int64 
 9   Cruelty by Husband or his Relatives                  10677 non-null  int64 
 10  Importation of Girls                                 10677 non-null  int64 
dtypes: int64(9), object(2)
memory usage: 917.7+ KB


## To get the summary statistics of all the numerical columns present in the data
data.describe()


#removing the unnamed column
data.drop('Unnamed: 0',axis=1,inplace=True)
data


## To check null values present in the dataset
data.isnull().sum()

STATE/UT                                               0
DISTRICT                                               0
Year                                                   0
Rape                                                   0
Kidnapping and Abduction                               0
Dowry Deaths                                           0
Assault on women with intent to outrage her modesty    0
Insult to modesty of Women                             0
Cruelty by Husband or his Relatives                    0
Importation of Girls                                   0
dtype: int64


#to see the unique states and union territories
data['STATE/UT'].unique()

array(['ANDHRA PRADESH', 'ARUNACHAL PRADESH', 'ASSAM', 'BIHAR',
       'CHHATTISGARH', 'GOA', 'GUJARAT', 'HARYANA', 'HIMACHAL PRADESH',
       'JAMMU & KASHMIR', 'JHARKHAND', 'KARNATAKA', 'KERALA',
       'MADHYA PRADESH', 'MAHARASHTRA', 'MANIPUR', 'MEGHALAYA', 'MIZORAM',
       'NAGALAND', 'ODISHA', 'PUNJAB', 'RAJASTHAN', 'SIKKIM',
       'TAMIL NADU', 'TRIPURA', 'UTTAR PRADESH', 'UTTARAKHAND',
       'WEST BENGAL', 'A & N ISLANDS', 'CHANDIGARH', 'D & N HAVELI',
       'DAMAN & DIU', 'DELHI', 'LAKSHADWEEP', 'PUDUCHERRY',
       'Andhra Pradesh', 'Arunachal Pradesh', 'Assam', 'Bihar',
       'Chhattisgarh', 'Goa', 'Gujarat', 'Haryana', 'Himachal Pradesh',
       'Jammu & Kashmir', 'Jharkhand', 'Karnataka', 'Kerala',
       'Madhya Pradesh', 'Maharashtra', 'Manipur', 'Meghalaya', 'Mizoram',
       'Nagaland', 'Odisha', 'Punjab', 'Rajasthan', 'Sikkim',
       'Tamil Nadu', 'Tripura', 'Uttar Pradesh', 'Uttarakhand',
       'West Bengal', 'A&N Islands', 'Chandigarh', 'D&N Haveli',
       'Daman & Diu', 'Delhi UT', 'Lakshadweep', 'Puducherry',
       'Telangana', 'A & N Islands'], dtype=object)


data['DISTRICT'].unique()

array(['ADILABAD', 'ANANTAPUR', 'CHITTOOR', ..., 'Lakshadweep',
       'Karaikal', 'Puducherry'], dtype=object)


data['Year'].unique()

array([2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
       2012, 2013, 2014], dtype=int64)


#since the states are repeated converting all states to upper and comibing the same states
data.loc[data['STATE/UT'] == 'A&N Islands', 'STATE/UT'] = 'A & N ISLANDS'
data.loc[data['STATE/UT'] == 'D&N Haveli', 'STATE/UT'] = 'D & N HAVELI'
data.loc[data['STATE/UT'] == 'Delhi UT', 'STATE/UT'] = 'DELHI'

#converting all the state names to capitals
data['STATE/UT'] = pd.Series(str.upper(i) for i in data['STATE/UT'])
data['DISTRICT'] = pd.Series(str.upper(i) for i in data['DISTRICT'])


#stroring the sum of all crimes comitted within a state statewise
state_all_crimes = data.groupby('STATE/UT').sum()


#droping the sum of year column
state_all_crimes.drop('Year',axis=1,inplace=True)


#adding a column containig the total crime against women in that state
col_list= list(state_all_crimes)
state_all_crimes['Total']=state_all_crimes[col_list].sum(axis=1)
all_crimes = state_all_crimes

all_crimes


#sorting the statewise crime from highest to lowest

state_all_crimes.sort_values('Total',ascending=False)
state_all_crimes


state_all_crimes.info()

<class 'pandas.core.frame.DataFrame'>
Index: 36 entries, A & N ISLANDS to WEST BENGAL
Data columns (total 8 columns):
 #   Column                                               Non-Null Count  Dtype
---  ------                                               --------------  -----
 0   Rape                                                 36 non-null     int64
 1   Kidnapping and Abduction                             36 non-null     int64
 2   Dowry Deaths                                         36 non-null     int64
 3   Assault on women with intent to outrage her modesty  36 non-null     int64
 4   Insult to modesty of Women                           36 non-null     int64
 5   Cruelty by Husband or his Relatives                  36 non-null     int64
 6   Importation of Girls                                 36 non-null     int64
 7   Total                                                36 non-null     int64
dtypes: int64(8)
memory usage: 2.5+ KB


state_all_crimes=state_all_crimes.reset_index()


##CRIME STATUS
#all crimes count
total_df=state_all_crimes.sum(axis=0).reset_index()
tf=pd.DataFrame(total_df)
tf


#for all crimes count
#tf = drop

tf=tf.drop([0])


tf=tf.drop([8])


import plotly.express as px                     ##provides functions to visualize a variety of types of data


#plotting
#state v/s total crimes

sorted_df = state_all_crimes.sort_values('Total',ascending=False)
fig = px.bar( x=tf["index"],y=tf[0], color=tf[0], 
             labels={'x': "Crimes", 'y': "Count"}, title="Total Cases", 
             color_continuous_scale='burg')
fig.show()


#state v/s total crimes
sorted_df = state_all_crimes.sort_values('Total',ascending=False)
fig = px.bar( x=sorted_df['STATE/UT'],y=sorted_df["Total"], color=sorted_df["Total"], 
             labels={'x': "States", 'y': "Count"}, title="Total Cases", 
             color_continuous_scale='burg')
fig.show()


#states v/s rapes

fig = px.bar( x=state_all_crimes['STATE/UT'],y=state_all_crimes["Rape"], color=state_all_crimes["Rape"], 
             labels={'x': "States", 'y': "Count"}, title="Rape Cases", 
             color_continuous_scale='burg')
fig.show()


#states v/s  kidnapping and abduction

fig = px.bar( x=state_all_crimes['STATE/UT'],y=state_all_crimes["Kidnapping and Abduction"], color=state_all_crimes["Kidnapping and Abduction"], 
             labels={'x': "States", 'y': "Count"}, title="Kidnapping and Abduction Cases", 
             color_continuous_scale='burg')
fig.show()


#states v/s  Dowry Deaths

fig = px.bar( x=state_all_crimes['STATE/UT'],y=state_all_crimes["Dowry Deaths"], color=state_all_crimes["Dowry Deaths"], 
             labels={'x': "States", 'y': "Count"}, title="Dowry Deaths", 
             color_continuous_scale='burg')
fig.show()


#states v/s  Assault on women with intent to outrage her modesty

fig = px.bar( x=state_all_crimes['STATE/UT'],y=state_all_crimes["Assault on women with intent to outrage her modesty"], color=state_all_crimes["Assault on women with intent to outrage her modesty"], 
             labels={'x': "States", 'y': "Count"}, title="Assault on women with intent to outrage her modesty", 
             color_continuous_scale='burg')
fig.show()


#states v/s Insult to modesty of Women

fig = px.bar( x=state_all_crimes['STATE/UT'],y=state_all_crimes["Insult to modesty of Women"], color=state_all_crimes["Insult to modesty of Women"], 
             labels={'x': "States", 'y': "Count"}, title="Insult to modesty of Women", 
             color_continuous_scale='burg')
fig.show()


#states v/s Cruelty by Husband or his Relatives

fig = px.bar( x=state_all_crimes['STATE/UT'],y=state_all_crimes["Cruelty by Husband or his Relatives"], color=state_all_crimes["Cruelty by Husband or his Relatives"], 
             labels={'x': "States", 'y': "Count"}, title="Cruelty by Husband or his Relatives", 
             color_continuous_scale='burg')
fig.show()


#states v/s Importation of Girls 
importation_df = state_all_crimes.copy() 
importation_df.loc[importation_df['Importation of Girls'] <= 50, 'STATE/UT'] = 'Others'
# Represent only large countries 
fig = px.pie(importation_df, values='Importation of Girls', names='STATE/UT', title="Importation of Girls", color_discrete_sequence=px.colors.sequential.Teal_r) 
fig.update_traces(textposition='inside', textinfo='label+value', marker=dict(line=dict(color='#000000', width=2)))
#fig.update_layout(annotations=[dict(text='count', x=0.5, y=0.5, font_size=20, showarrow=False)]) 
fig.show()


plt.figure(figsize=(15,7))
data.groupby("Year")["Rape"].sum().plot()
data.groupby("Year")["Kidnapping and Abduction"].sum().plot()
data.groupby("Year")["Dowry Deaths"].sum().plot(label="Dowry Deaths")
data.groupby("Year")["Assault on women with intent to outrage her modesty"].sum().plot()
data.groupby("Year")["Insult to modesty of Women"].sum().plot()
data.groupby("Year")["Cruelty by Husband or his Relatives"].sum().plot()
data.groupby("Year")["Importation of Girls"].sum().plot()
plt.legend()
plt.tight_layout()


all_crimes = all_crimes.reset_index()
all_crimes


all_crimes.shape

(36, 9)


#finding the mean number of crimes

m = all_crimes['Total'].mean()
print('mean=',m)


#finding the quantiles 
q = np.quantile(all_crimes['Total'],[0.25,0.75])
print(q)
l=q[0]
u=q[1]

mean= 147822.5
[  5060.5 211358.5]


#copying the state_all_crimes to a new dataframe to normalise values and predict
df_kmeans = all_crimes.loc[:,all_crimes.columns!="STATE/UT"]

#adding an additional column called output
output=[]
for i in df_kmeans['Total']:
    if i >= m:
        output.append(1)#redzone
    elif m > i:
        output.append(0)#safe

all_crimes['output']=output
df_kmeans_y=all_crimes['output']


from sklearn.preprocessing import MinMaxScaler
cols = df_kmeans.columns
cols

Index(['Rape', 'Kidnapping and Abduction', 'Dowry Deaths',
       'Assault on women with intent to outrage her modesty',
       'Insult to modesty of Women', 'Cruelty by Husband or his Relatives',
       'Importation of Girls', 'Total'],
      dtype='object')


ms = MinMaxScaler()

df_kmeans = ms.fit_transform(df_kmeans)


df_kmeans = pd.DataFrame(df_kmeans,columns=[cols])
df_kmeans.head()


from sklearn.cluster import KMeans

kmeans = KMeans(n_clusters=2, random_state=0) 

kmeans.fit(df_kmeans)

KMeans(n_clusters=2, random_state=0)


kmeans.inertia_

6.49248832628424


#checking the accuracy

labels = kmeans.labels_

# check how many of the samples were correctly labeled
correct_labels = sum(df_kmeans_y == labels)

print('labels:',labels)
print('df_kmeans output:',df_kmeans_y)
print("Result: %d out of %d samples were correctly labeled." % (correct_labels, df_kmeans_y.size))

labels: [0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1]
df_kmeans output: 0     0
1     1
2     0
3     1
4     1
5     0
6     0
7     0
8     0
9     1
10    0
11    1
12    1
13    0
14    0
15    0
16    1
17    1
18    0
19    1
20    1
21    0
22    0
23    0
24    0
25    1
26    0
27    0
28    1
29    0
30    1
31    0
32    0
33    1
34    0
35    1
Name: output, dtype: int64
Result: 27 out of 36 samples were correctly labeled.


#based on the prediction of the k means algorithm classifying the states 
#as safe or unsafe for women

final=[]
for i in range(len(labels)):
    state=all_crimes['STATE/UT'][i]
    label = labels[i]
    if label == 1:
        final.append([state,'unsafe'])
        
    else:
        final.append([state,'safe'])
        

final_df = pd.DataFrame(final, columns=['STATES/UT', 'SAFE/UNSAFE'])
  
final_df

	Unnamed: 0	Year	Rape	Kidnapping and Abduction	Dowry Deaths	Assault on women with intent to outrage her modesty	Insult to modesty of Women	Cruelty by Husband or his Relatives	Importation of Girls
count	10677.000000	10677.000000	10677.000000	10677.000000	10677.000000	10677.000000	10677.000000	10677.000000	10677.000000
mean	3871.570198	2007.697949	57.989885	69.888358	20.181699	113.539196	27.419313	209.224314	0.175330
std	2816.369918	4.046874	214.230398	311.623450	98.276531	458.903951	167.806797	905.664362	2.228637
min	0.000000	2001.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	1009.000000	2004.000000	8.000000	6.000000	1.000000	10.000000	0.000000	11.000000	0.000000
50%	3678.000000	2008.000000	22.000000	20.000000	5.000000	34.000000	2.000000	50.000000	0.000000
75%	6347.000000	2011.000000	44.000000	49.000000	16.000000	85.000000	12.000000	144.000000	0.000000
max	9016.000000	2014.000000	5076.000000	10626.000000	2469.000000	10001.000000	4970.000000	23278.000000	83.000000

	Rape	Kidnapping and Abduction	Dowry Deaths	Assault on women with intent to outrage her modesty	Insult to modesty of Women	Cruelty by Husband or his Relatives	Importation of Girls	Total
STATE/UT
A & N ISLANDS	336	212	20	600	112	288	0	1568
ANDHRA PRADESH	32150	34504	13844	126952	86964	280906	34	575354
ARUNACHAL PRADESH	1316	1470	6	1996	48	476	0	5312
ASSAM	40190	62074	3268	38256	254	115300	22	259364
BIHAR	30758	57086	32206	16958	694	69770	904	208376
CHANDIGARH	770	1682	90	1088	342	2080	0	6052
CHHATTISGARH	29308	11808	2758	47096	3968	23436	12	118386
D & N HAVELI	132	224	2	106	12	90	0	566
DAMAN & DIU	60	44	6	40	4	76	0	230
DELHI	20312	46586	3758	30742	7928	42834	2	152162
GOA	1062	640	38	1384	468	532	0	4124
GUJARAT	11644	34670	1108	23186	3230	146468	0	220306
HARYANA	17110	20016	7372	17142	17866	68414	4	147924
HIMACHAL PRADESH	4674	4116	112	9256	1220	7796	0	27174
JAMMU & KASHMIR	7038	21164	294	28638	9168	5390	0	71692
JHARKHAND	22826	14186	7896	9898	566	23910	298	79580
KARNATAKA	15056	16262	7016	65750	2276	72706	94	179160
KERALA	20030	4452	700	80438	7930	111626	0	225176
LAKSHADWEEP	20	2	0	14	4	14	0	54
MADHYA PRADESH	90996	35608	21090	195478	20964	102816	134	467086
MAHARASHTRA	48974	30368	9696	114704	32634	193202	6	429584
MANIPUR	1068	2606	6	1322	6	578	0	5586
MEGHALAYA	2642	670	36	1470	124	460	8	5410
MIZORAM	2070	30	8	1988	70	134	6	4306
NAGALAND	562	190	2	260	24	32	2	1072
ODISHA	30480	25588	10782	80214	7488	49206	36	203794
PUDUCHERRY	208	306	56	1314	600	234	0	2718
PUNJAB	14656	15096	3524	12410	1728	30840	4	78258
RAJASTHAN	45684	66278	11854	83362	794	262200	14	470186
SIKKIM	570	180	4	586	34	108	0	1482
TAMIL NADU	16660	30908	5060	43404	21170	45524	30	162756
TELANGANA	1958	1422	578	6376	2284	12738	0	25356
TRIPURA	5060	2202	752	7344	114	16086	0	31558
UTTAR PRADESH	51150	135906	57256	91212	53130	193738	6	582398
UTTARAKHAND	3752	6484	1974	4366	3190	9756	2	29524
WEST BENGAL	47876	61158	12308	66908	5348	344124	254	537976

	Rape	Kidnapping and Abduction	Dowry Deaths	Assault on women with intent to outrage her modesty	Insult to modesty of Women	Cruelty by Husband or his Relatives	Importation of Girls	Total
STATE/UT
A & N ISLANDS	336	212	20	600	112	288	0	1568
ANDHRA PRADESH	32150	34504	13844	126952	86964	280906	34	575354
ARUNACHAL PRADESH	1316	1470	6	1996	48	476	0	5312
ASSAM	40190	62074	3268	38256	254	115300	22	259364
BIHAR	30758	57086	32206	16958	694	69770	904	208376
CHANDIGARH	770	1682	90	1088	342	2080	0	6052
CHHATTISGARH	29308	11808	2758	47096	3968	23436	12	118386
D & N HAVELI	132	224	2	106	12	90	0	566
DAMAN & DIU	60	44	6	40	4	76	0	230
DELHI	20312	46586	3758	30742	7928	42834	2	152162
GOA	1062	640	38	1384	468	532	0	4124
GUJARAT	11644	34670	1108	23186	3230	146468	0	220306
HARYANA	17110	20016	7372	17142	17866	68414	4	147924
HIMACHAL PRADESH	4674	4116	112	9256	1220	7796	0	27174
JAMMU & KASHMIR	7038	21164	294	28638	9168	5390	0	71692
JHARKHAND	22826	14186	7896	9898	566	23910	298	79580
KARNATAKA	15056	16262	7016	65750	2276	72706	94	179160
KERALA	20030	4452	700	80438	7930	111626	0	225176
LAKSHADWEEP	20	2	0	14	4	14	0	54
MADHYA PRADESH	90996	35608	21090	195478	20964	102816	134	467086
MAHARASHTRA	48974	30368	9696	114704	32634	193202	6	429584
MANIPUR	1068	2606	6	1322	6	578	0	5586
MEGHALAYA	2642	670	36	1470	124	460	8	5410
MIZORAM	2070	30	8	1988	70	134	6	4306
NAGALAND	562	190	2	260	24	32	2	1072
ODISHA	30480	25588	10782	80214	7488	49206	36	203794
PUDUCHERRY	208	306	56	1314	600	234	0	2718
PUNJAB	14656	15096	3524	12410	1728	30840	4	78258
RAJASTHAN	45684	66278	11854	83362	794	262200	14	470186
SIKKIM	570	180	4	586	34	108	0	1482
TAMIL NADU	16660	30908	5060	43404	21170	45524	30	162756
TELANGANA	1958	1422	578	6376	2284	12738	0	25356
TRIPURA	5060	2202	752	7344	114	16086	0	31558
UTTAR PRADESH	51150	135906	57256	91212	53130	193738	6	582398
UTTARAKHAND	3752	6484	1974	4366	3190	9756	2	29524
WEST BENGAL	47876	61158	12308	66908	5348	344124	254	537976

	index	0
0	STATE/UT	A & N ISLANDSANDHRA PRADESHARUNACHAL PRADESHAS...
1	Rape	619158
2	Kidnapping and Abduction	746198
3	Dowry Deaths	215480
4	Assault on women with intent to outrage her mo...	1212258
5	Insult to modesty of Women	292756
6	Cruelty by Husband or his Relatives	2233888
7	Importation of Girls	1872
8	Total	5321610

	STATE/UT	Rape	Kidnapping and Abduction	Dowry Deaths	Assault on women with intent to outrage her modesty	Insult to modesty of Women	Cruelty by Husband or his Relatives	Importation of Girls	Total
0	A & N ISLANDS	336	212	20	600	112	288	0	1568
1	ANDHRA PRADESH	32150	34504	13844	126952	86964	280906	34	575354
2	ARUNACHAL PRADESH	1316	1470	6	1996	48	476	0	5312
3	ASSAM	40190	62074	3268	38256	254	115300	22	259364
4	BIHAR	30758	57086	32206	16958	694	69770	904	208376
5	CHANDIGARH	770	1682	90	1088	342	2080	0	6052
6	CHHATTISGARH	29308	11808	2758	47096	3968	23436	12	118386
7	D & N HAVELI	132	224	2	106	12	90	0	566
8	DAMAN & DIU	60	44	6	40	4	76	0	230
9	DELHI	20312	46586	3758	30742	7928	42834	2	152162
10	GOA	1062	640	38	1384	468	532	0	4124
11	GUJARAT	11644	34670	1108	23186	3230	146468	0	220306
12	HARYANA	17110	20016	7372	17142	17866	68414	4	147924
13	HIMACHAL PRADESH	4674	4116	112	9256	1220	7796	0	27174
14	JAMMU & KASHMIR	7038	21164	294	28638	9168	5390	0	71692
15	JHARKHAND	22826	14186	7896	9898	566	23910	298	79580
16	KARNATAKA	15056	16262	7016	65750	2276	72706	94	179160
17	KERALA	20030	4452	700	80438	7930	111626	0	225176
18	LAKSHADWEEP	20	2	0	14	4	14	0	54
19	MADHYA PRADESH	90996	35608	21090	195478	20964	102816	134	467086
20	MAHARASHTRA	48974	30368	9696	114704	32634	193202	6	429584
21	MANIPUR	1068	2606	6	1322	6	578	0	5586
22	MEGHALAYA	2642	670	36	1470	124	460	8	5410
23	MIZORAM	2070	30	8	1988	70	134	6	4306
24	NAGALAND	562	190	2	260	24	32	2	1072
25	ODISHA	30480	25588	10782	80214	7488	49206	36	203794
26	PUDUCHERRY	208	306	56	1314	600	234	0	2718
27	PUNJAB	14656	15096	3524	12410	1728	30840	4	78258
28	RAJASTHAN	45684	66278	11854	83362	794	262200	14	470186
29	SIKKIM	570	180	4	586	34	108	0	1482
30	TAMIL NADU	16660	30908	5060	43404	21170	45524	30	162756
31	TELANGANA	1958	1422	578	6376	2284	12738	0	25356
32	TRIPURA	5060	2202	752	7344	114	16086	0	31558
33	UTTAR PRADESH	51150	135906	57256	91212	53130	193738	6	582398
34	UTTARAKHAND	3752	6484	1974	4366	3190	9756	2	29524
35	WEST BENGAL	47876	61158	12308	66908	5348	344124	254	537976

CRIME AGAINST WOMEN IN INDIA 2001-2014¶

The main objective of this project is to classifying states as safe or unsafe¶

EXPLORATORY DATA ANALYSIS¶

CHECKING NULL VALUES¶

creating a data frame for statewise analysis¶

Analysis¶

visualsing each crime statewise¶

Observation¶

Observation¶

Analysing the each crime statewise¶

Observation¶

Observation¶

Observation¶

Observation¶

Observation¶

Observation¶

Observation¶

CLUSTERING¶

FEATURE SCALING¶

K-MEANS MODELLING¶

Observation¶

	Unnamed: 0	STATE/UT	DISTRICT	Year	Rape	Kidnapping and Abduction	Dowry Deaths	Assault on women with intent to outrage her modesty	Insult to modesty of Women	Cruelty by Husband or his Relatives
0	0	ANDHRA PRADESH	ADILABAD	2001	50	30	16	149	34	175
1	1	ANDHRA PRADESH	ANANTAPUR	2001	23	30	7	118	24	154
2	2	ANDHRA PRADESH	CHITTOOR	2001	27	34	14	112	83	186
3	3	ANDHRA PRADESH	CUDDAPAH	2001	20	20	17	126	38	57
4	4	ANDHRA PRADESH	EAST GODAVARI	2001	23	26	12	109	58	247

	Unnamed: 0	STATE/UT	DISTRICT	Year	Rape	Kidnapping and Abduction	Dowry Deaths	Assault on women with intent to outrage her modesty	Insult to modesty of Women	Cruelty by Husband or his Relatives
10672	832	Lakshadweep	Lakshadweep	2014	1	0	0	1	2	0
10673	833	Lakshadweep	Total District(s)	2014	1	0	0	1	2	0
10674	834	Puducherry	Karaikal	2014	3	1	0	12	1	1
10675	835	Puducherry	Puducherry	2014	7	6	1	20	7	3
10676	836	Puducherry	Total District(s)	2014	10	7	1	32	8	4

	Rape	Kidnapping and Abduction	Dowry Deaths	Assault on women with intent to outrage her modesty	Insult to modesty of Women	Cruelty by Husband or his Relatives	Importation of Girls	Total
0	0.003473	0.001545	0.000349	0.002998	0.001242	0.000796	0.000000	0.002600
1	0.353170	0.253870	0.241791	0.649419	1.000000	0.816285	0.037611	0.987904
2	0.014246	0.010802	0.000105	0.010140	0.000506	0.001343	0.000000	0.009029
3	0.441545	0.456734	0.057077	0.195647	0.002875	0.335027	0.024336	0.445287
4	0.337869	0.420032	0.562491	0.086686	0.007935	0.202714	1.000000	0.357730

	STATES/UT	SAFE/UNSAFE
0	A & N ISLANDS	safe
1	ANDHRA PRADESH	unsafe
2	ARUNACHAL PRADESH	safe
3	ASSAM	safe
4	BIHAR	safe
5	CHANDIGARH	safe
6	CHHATTISGARH	safe
7	D & N HAVELI	safe
8	DAMAN & DIU	safe
9	DELHI	safe
10	GOA	safe
11	GUJARAT	safe
12	HARYANA	safe
13	HIMACHAL PRADESH	safe
14	JAMMU & KASHMIR	safe
15	JHARKHAND	safe
16	KARNATAKA	safe
17	KERALA	safe
18	LAKSHADWEEP	safe
19	MADHYA PRADESH	unsafe
20	MAHARASHTRA	unsafe
21	MANIPUR	safe
22	MEGHALAYA	safe
23	MIZORAM	safe
24	NAGALAND	safe
25	ODISHA	safe
26	PUDUCHERRY	safe
27	PUNJAB	safe
28	RAJASTHAN	unsafe
29	SIKKIM	safe
30	TAMIL NADU	safe
31	TELANGANA	safe
32	TRIPURA	safe
33	UTTAR PRADESH	unsafe
34	UTTARAKHAND	safe
35	WEST BENGAL	unsafe